Brain Tumor Identification using Transfer Learning with Sugeno-Fuzzy Integral

Brain Tumor Identification using Transfer Learning with Sugeno-Fuzzy Integral | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Brain Tumor Identification using Transfer Learning with Sugeno-Fuzzy Integral Nikhil Govil, Shailee Lohmor Choudhary, Rinku Sharma Dixit, Saurabh Anand, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4108109/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Brain tumor identification is essential in determining the cause and treatment of brain tumors, which are abnormal cell growths in the brain. The identification of brain cancers early and accurately is critical for prompt management and better patient outcomes. Significant advancement has been made in the invention of computer-aided detection systems that use sophisticated imaging methods and ML algorithms for automated brain tumor diagnosis in recent years. We provide a strategy for classifying brain tumor images into Pituitary, Glioma, and Meningioma tumors using a Sugeno fuzzy integral ensemble approach with three transfer learning approaches, namely ResNet-164, SqueezeNet, and DenseNet-201. In terms of accuracy, the proposed fuzzy ensemble strategies exceed each separate transfer learning approach. The proposed DenseNet-201 combined with SFI ensemble model has an accuracy rating of 99.19%. This framework was used to detect brain tumors in the current study, but it might potentially be built and used for medical imaging assessments of other illnesses. This solution improves the diagnostic process's efficiency and automation in the healthcare business, saving time and improving accuracy in brain tumor detection. Biological sciences/Cancer Biological sciences/Neuroscience Earth and environmental sciences/Biogeochemistry Health sciences/Molecular medicine Health sciences/Oncology Physical sciences/Energy science and technology Brain tumor Meningioma Pituitary ResNet-164 SqueezeNe Transfer learning DenseNet-201 Sugeno fuzzy integral Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4108109","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":288849410,"identity":"4210b371-bc7e-4208-b254-bd7515f959ad","order_by":0,"name":"Nikhil Govil","email":"","orcid":"","institution":"IET, GLA University","correspondingAuthor":false,"prefix":"","firstName":"Nikhil","middleName":"","lastName":"Govil","suffix":""},{"id":288849411,"identity":"fb5528db-c75e-4525-aa64-dc71af431280","order_by":1,"name":"Shailee Lohmor Choudhary","email":"","orcid":"","institution":"New Delhi Institute of Management, New 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